About the project
The aim of the project is to improve the interconnection of the Research Infrastructures within the Network University Medicine (NUM) and to make them more accessible to researchers. Through a shared access portal, standardised structures and the integration of further data sources, the project aims to facilitate the use of the NUM infrastructure and support its long-term development.
This gives rise to the following overarching objectives:
- Improving the visibility and accessibility of the NUM’s Research Infrastructures
- Creating centralised and standardised access to the Research Infrastructures
- Strengthening the interconnection and harmonisation of existing research platforms
- Expanding the NUM data repository to include additional data sources
- Establishment of sustainable and scalable structures for future expansion
- Promotion of the use of the NUM infrastructure in research projects
Key points at a glance
The project aims to improve the interconnection of the Research Infrastructures within the Network University Medicine (NUM) and to make them more accessible to researchers. Through a shared access portal, standardised structures and the integration of further data sources, the aim is to facilitate the use of the NUM infrastructure and support its long-term development.
This gives rise to the following overarching objectives:
- Improving the visibility and accessibility of the NUM’s Research Infrastructures
- Creating centralised and standardised access to the Research Infrastructures
- Strengthening the interconnection and harmonisation of existing research platforms
- Expanding the NUM data repository to include additional data sources
- Establishment of sustainable and scalable structures for future expansion
- Promotion of the use of the NUM infrastructure in research projects
Numerous Research Infrastructures and solutions have already been developed as part of earlier sub-projects, some of which are still ongoing. These were each created to fulfil specific project objectives and address different use cases and data sources, such as routine data, study data or post-mortem data. As a result, a diverse but fragmented system landscape has developed over time, the components of which are only interconnected to a limited extent. This hinders uniform access to the Research Infrastructures, leads to inconsistencies in data and processes, and complicates the sustainable and scalable further development of the infrastructure.
A graph-based approach is used to describe and organise the available research resources. This means that information is not merely stored in tabular form, but is represented as a network: individual data points and concepts are linked together, making connections between different pieces of information visible. This enables researchers both to search specifically for particular data and to discover new connections and relevant resources. Unlike traditional databases, such a knowledge graph allows relationships between different concepts to be mapped more effectively. In addition, standardised terminology and technical standards are used so that data and information from different sources can be described consistently and compared with one another. The knowledge graph serves as a central overview of NUM’s research resources and systematically links the available information.
Implementation is taking place in three stages:
1. Firstly, a comprehensive overview of the existing research resources is being compiled. This involves gathering information on which data types are available, how the data was collected and where it can be accessed.
2. Subsequently, additional information will be added, for example regarding the scope of existing datasets (e.g. the number of available imaging data sets or the size of study groups).
3. In the final stage, search tools are developed to enable researchers to check specifically whether suitable data or patient groups are available for particular research questions – for example, based on defined criteria.
The actual research data remains stored at the respective institutions. Only standardised descriptions of the data are exchanged and linked. This makes it easier to find and utilise existing research resources, whilst control over the data remains with the respective institutions.